Web: http://arxiv.org/abs/2106.09946

Sept. 22, 2022, 1:13 a.m. | Sauptik Dhar, Javad Heydari, Samarth Tripathi, Unmesh Kurup, Mohak Shah

stat.ML updates on arXiv.org arxiv.org

Limited availability of labeled-data makes any supervised learning problem
challenging. Alternative learning settings like semi-supervised and universum
learning alleviate the dependency on labeled data, but still require a large
amount of unlabeled data, which may be unavailable or expensive to acquire.
GAN-based data generation methods have recently shown promise by generating
synthetic samples to improve learning. However, most existing GAN based
approaches either provide poor discriminator performance under limited labeled
data settings; or results in low quality generated data. In …

arxiv gans

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